基于SVD-SVM的HVDC换流器故障诊断  

Fault Diagnosis for HVDC Converters Based on SVD-SVM

作　　者：刘霄涵[1] 王渝红[1] 胡松伶 李兴源[1] 朱艳[1] 

机构地区：[1]四川大学电气信息学院,成都610065

出　　处：《高压电器》2016年第10期86-91,共6页High Voltage Apparatus

基　　金：国家自然科学基金资助项目(51037003)~~

摘　　要：换流器结构复杂,其故障信号难以诊断。文中基于奇异值分解(SVD)和支持向量机(SVM)提出了一种换流器故障诊断的新方法,对选取的故障信号矩阵进行SVD分解,所得奇异值的大小反映故障信息量的大小,选取最大奇异值对应的特征矩阵作为样本,用SVM进行训练分类。当换流器发生故障时,对故障信号矩阵进行SVD分解,用训练所得的SVM诊断器进行故障诊断。仿真表明,SVD分解可以有效提取换流器故障特征,通过SVM可以准确诊断换流器各种故障,文中方法快速准确。Converter is one of the core equipment in HVDC systems, and converter faults are complex and difficult to diagnose. Based on singular value decomposition （SVD）, the fault information is indicated by sin- gular values. The characteristic matrix of the largest singular value is selected to train the support vector machine（SVM）diagnotor. When faults occur in converters, characteristic matrix of the largest singular value can be calculated and then be input into the trained support vector machine classifier to diagnose. Simulation shows that the method can extract features of different converter faults, and distinguish different converter faults effectively and correctly.

关 键 词：高压直流输电 换流器 奇异值分解 支持向量机 故障诊断 

分 类 号：TM721.1[电气工程—电力系统及自动化]